Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions
This is an incremental work that surveys existing methods for optimizing compound AI systems, addressing challenges for researchers and practitioners in AI system design.
This survey paper tackles the problem of optimizing complex AI workflows that integrate multiple components, known as compound AI systems, by reviewing recent progress in methods like supervised fine-tuning, reinforcement learning, and natural language feedback, and it provides a systematic classification and highlights open challenges in this field.
Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple components, compound AI systems have become increasingly adept at performing sophisticated tasks. However, as these systems grow in complexity, new challenges arise in optimizing not only individual components but also their interactions. While traditional optimization methods such as supervised fine-tuning (SFT) and reinforcement learning (RL) remain foundational, the rise of natural language feedback introduces promising new approaches, especially for optimizing non-differentiable systems. This paper provides a systematic review of recent progress in optimizing compound AI systems, encompassing both numerical and language-based techniques. We formalize the notion of compound AI system optimization, classify existing methods along several key dimensions, and highlight open research challenges and future directions in this rapidly evolving field. A list of surveyed papers is publicly available at https://github.com/MiuLab/AISysOpt-Survey.